Compare models

Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

Gemini 3.8 FlashGoogle Remove
Muse Spark 1.3Meta Remove
Llama 3.2 1b InstructMeta Remove

Add a model

gemini-3.8-flash vs muse-spark-1.3 vs llama-3.2-1b-instruct
Attribute	Gemini 3.8 Flashgemini-3.8-flash	Muse Spark 1.3muse-spark-1.3	Llama 3.2 1b Instructllama-3.2-1b-instruct
Pricing
Input	$0.75 / 1M	$1.25 / 1M	$0.0025 / 1M
Output	$3.75 / 1M	$4.25 / 1M	$0.005 / 1M
Cache Write (5m)	$0.75 / 1M	$1.25 / 1M	$0.0025 / 1M
Cache Write (1h)	$0.75 / 1M	$1.25 / 1M	$0.0025 / 1M
Cache Read	$0.75 / 1M	$1.25 / 1M	$0.0025 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	1M	N/A
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes
Streaming	Yes	Yes	Yes
Catalogue
Provider	Google	Meta	Meta
Category	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—
Description
Summary	Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.	Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.	Llama 3.2 1B is a lightweight 1-billion-parameter model built for efficient NLP tasks like summarization, conversation, and multilingual analysis. It runs well in low-resource environments, supports eight core languages (and can be fine-tuned for more), making it a good fit for developers who need capable, multilingual AI without heavy compute costs.